Enhancing deep diffractive neural networks via nonlinear metasurfaces

Linzhi Yu; Atanas Gotchev; Humeyra Caglayan

doi:10.1117/12.3016373

18 June 2024 Enhancing deep diffractive neural networks via nonlinear metasurfaces

Linzhi Yu, Atanas Gotchev, Humeyra Caglayan

Proceedings Volume PC13017, Machine Learning in Photonics; PC130170U (2024) https://doi.org/10.1117/12.3016373
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Conference Poster

Abstract

Free-space optical neural networks (ONNs) are promising for their potential in intricate, high-resolution image processing. However, the introduction of nonlinear units, which are crucial for advancing the capabilities of free-space ONNs, poses a significant technical obstacle. Our research explores the feasibility of integrating nonlinear metasurfaces with free-space ONNs to preserve their high-throughput, low-loss advantages while enabling enhanced capabilities. We specifically examine the role of nonlinear amplitude modulation via metasurfaces in enhancing ONN performance. Through simulations, we evaluate its impact on the network's capability in pattern recognition task.

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Linzhi Yu, Atanas Gotchev, and Humeyra Caglayan "Enhancing deep diffractive neural networks via nonlinear metasurfaces", Proc. SPIE PC13017, Machine Learning in Photonics, PC130170U (18 June 2024); https://doi.org/10.1117/12.3016373

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